Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Article


Shahverdi, H., Nabati, M., Fard Moshiri, P., Asvadi, R. and Ghorashi, S. 2023. Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques. Information . 14 (7), p. 404. https://doi.org/https://doi.org/10.3390/info14070404
AuthorsShahverdi, H., Nabati, M., Fard Moshiri, P., Asvadi, R. and Ghorashi, S.
Abstract

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.

Keywordshuman activity recognition; Internet of Things; deep learning; channel state information; convolutional neural networks
JournalInformation
Journal citation14 (7), p. 404
ISSN2078-2489
Year2023
PublisherMDPI
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Anyone
Digital Object Identifier (DOI)https://doi.org/https://doi.org/10.3390/info14070404
Publication dates
Online14 Jul 2023
Publication process dates
Accepted07 Jul 2023
Deposited09 Aug 2023
Copyright holder© 2023, The Author(s)
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